A hierarchical distance sampling model to estimate spatially explicit sea otter density. Issue 9 (17th September 2021)

• Record Type: Journal Article
• Authors: Wilson, Ryan R.; St. Martin, Michelle; Beatty, William S.
• Is Part Of: Ecosphere. Volume 12:Issue 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-17
• Subjects: Aleutian Islands -- Bayesian -- distance sampling -- Eastern Aleutians Management Unit -- hierarchical modeling -- population estimation -- sea otter -- Southwest Sea Otter Distinct Population Segment; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.3666
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Wildlife managers often rely on analyses conducted prior to the widespread adoption of hierarchical models which can lead to questions about the accuracy of previous inferences. Hierarchical models allow observed data to be partitioned into factors that influenced the collection of the data such as detectability of animals (i.e., observation processes) and factors that influence the ecology of a population such as features that affect the distribution of animals (i.e., ecological processes). Population surveys for sea otters in the Aleutian Islands, Alaska, have historically been conducted by conflating the observation and ecological processes potentially leading to inaccurate population estimates. Based on boat and plane‐based sea otter survey data collected in 2017, we sought to overcome many problems of previous sea otter surveys in southwestern Alaska. We developed a spatially explicit hierarchical distance sampling model to estimate the abundance of sea otters in the Eastern Aleutians Management Unit while explicitly accounting for factors that affect the ability to detect sea otters during surveys (i.e., group size, ocean conditions). We also sought to account for the environmental factors leading to the non‐uniform distribution of sea otter groups by identifying relationships between otter group abundance and environmental attributes (i.e., ocean depth, presence of kelp, underwater substrate). Detection of sea otter groups was related to group size, and ocean conditions (e.g., ocean swell size). After accounting for detection, we estimated a mean population size of 8593 individual sea otters (95% CI: 7450–9984) which is considerably higher than previous estimates, although comparisons are difficult given divergent methodologies. Sea otter group density was negatively related to ocean depth and the presence of rock and gravel as underwater substrates. Conversely, sea otter group density was positively related to the presence of kelp and mud as an underwater substrate. Our hierarchical distance sampling model accounted for the observation process which allowed better estimates of the environmental attributes affecting otter abundance. Our research can serve as a template for other study systems requiring spatially explicit density estimates from a distance sampling framework and can help provide a new baseline for managers to gauge future population changes in sea otters.